In the rapidly evolving landscape of 5G technology, safeguarding Radio Frequency (RF) environments against sophisticated intrusions is paramount, especially in dynamic spectrum access and management. This paper presents an enhanced experimental model that integrates a self-attention mechanism with a Recurrent Neural Network (RNN)-based autoencoder for the detection of anomalous spectral activities in 5G networks at the waveform level. Our approach, grounded in time-series analysis, processes in-phase and quadrature (I/Q) samples to identify irregularities that could indicate potential jamming attacks. The model's architecture, augmented with a self-attention layer, extends the capabilities of RNN autoencoders, enabling a more nuanced understanding of temporal dependencies and contextual relationships within the RF spectrum. Utilizing a simulated 5G Radio Access Network (RAN) test-bed constructed with srsRAN 5G and Software Defined Radios (SDRs), we generated a comprehensive stream of data that reflects real-world RF spectrum conditions and attack scenarios. The model is trained to reconstruct standard signal behavior, establishing a normative baseline against which deviations, indicative of security threats, are identified. The proposed architecture is designed to balance between detection precision and computational efficiency, so the LSTM network, enriched with self-attention, continues to optimize for minimal execution latency and power consumption. Conducted on a real-world SDR-based testbed, our results demonstrate the model's improved performance and accuracy in threat detection. Keywords: self-attention, real-time intrusion detection, RNN autoencoder, Transformer architecture, LSTM, time series anomaly detection, 5G Security, spectrum access security.

翻译：在5G技术快速发展的背景下，保护射频环境免受复杂入侵至关重要，尤其是在动态频谱接入与管理场景中。本文提出一种增强型实验模型，该模型将自注意力机制与基于循环神经网络的自编码器相结合，用于在波形层面检测5G网络中的异常频谱活动。我们的方法基于时间序列分析，通过处理同相与正交分量样本，识别可能指示潜在干扰攻击的异常模式。该模型架构通过引入自注意力层，拓展了RNN自编码器的能力，使其能够更细致地理解射频频谱中的时间依赖性与上下文关系。利用基于srsRAN 5G和软件定义无线电构建的模拟5G无线接入网络测试平台，我们生成了反映真实射频频谱条件与攻击场景的综合性数据流。该模型通过重构标准信号行为进行训练，建立用于识别安全威胁指示性偏差的规范基线。所提出的架构在检测精度与计算效率之间取得平衡，经自注意力增强的LSTM网络持续优化执行延迟与功耗指标。在基于真实SDR的测试平台上进行的实验表明，该模型在威胁检测方面具有更优的性能与准确性。关键词：自注意力，实时入侵检测，RNN自编码器，Transformer架构，LSTM，时间序列异常检测，5G安全，频谱接入安全。